The role of digital tools and emerging devices in COVID-19 contact tracing during the first 18 months of the pandemic: a systematic review

Abstract Background Contact tracing is a public health intervention implemented in synergy with other preventive measures to curb epidemics, like the coronavirus pandemic. The development and use of digital devices have increased worldwide to enhance the contact tracing process. The aim of the study was to evaluate the effectiveness and impact of tracking coronavirus disease 2019 (COVID-19) patients using digital solutions. Methods Observational studies on digital contact tracing (DCT), published 2020–21, in English were identified through a systematic literature review performed on nine online databases. An ad hoc form was used for data extraction of relevant information. Quality assessment of the included studies was performed with validated tools. A qualitative synthesis of the findings is reported. Results Over 8000 records were identified and 37 were included in the study: 24 modelling and 13 population-based studies. DCT improved the identification of close contacts of COVID-19 cases and reduced the effective reproduction number of COVID-19-related infections and deaths by over 60%. It impacted positively on societal and economic costs, in terms of lockdowns and use of resources, including staffing. Privacy and security issues were reported in 27 studies. Conclusions DCT contributed to curbing the COVID-19 pandemic, especially with the high uptake rate of the devices and in combination with other public health measures, especially conventional contact tracing. The main barriers to the implementation of the devices are uptake rate, security and privacy issues. Public health digitalization and contact tracing are the keys to countries’ emergency preparedness for future health crises.


C
ontact tracing is the process of identifying and managing individuals who have been in the proximity of a person diagnosed with an infectious disease, in order to prevent additional transmission.It is 'an essential public health measure to fight the coronavirus disease 2019 (COVID-19) pandemic, in conjunction with active case finding and testing and in synergy with other measures such as physical distancing'. 12][3][4] The use of emerging devices (e.g.smartphones), in combination with traditional methods of contact tracing, has offered new potential for health authorities to limit or interrupt chains of SARS-CoV-2 transmission. 1Such solutions are also featured as public advocacy measures and a means to adjust national public health and social measures. 5igital contact tracing (DCT) typically uses smartphones or other devices (e.g.drones, eBracelets, thermal scans, artificial intelligence-based tools) and online platforms to monitor interactions between individuals and issue real-time alerts in case of contacts with COVID-19 cases.These devices are also deployed for patients' remote management, research activities and the implementation of public health measures. 6he effectiveness of DCT depends on its integration into wellestablished testing and contact tracing infrastructures. 2 Using digital tools in support of a comprehensive contact tracing strategy has shown promising results. 1,5However, there are still aspects that need to be addressed, such as the overall effectiveness of DCT solutions, including comparisons between manual and DCT, the impact of the community uptake, data privacy and security. 7Although the above questions have been studied from individual studies, a current systematic review that summarizes these findings is lacking. 8To this end, the effectiveness and impact of digital tools and emerging devices in COVID-19 contact tracing and their potential role in future health emergencies were assessed within the framework of the 'Population Health Information Research Infrastructure' (PHIRI) 9 PHIRI was developed to facilitate and generate the best available evidence for research on the health and well-being of populations impacted by COVID-19.

Methods
The study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement. 10The search string (coronavir � OR corona virus � OR corona pandemic � OR betacoronavir � OR covid19 OR covid OR nCoV OR novel CoV OR CoV2 OR sarscov2 OR sars2 OR 2019nCoV OR wuhan virus � ) AND (contact tracing OR contact tracing tool � OR contact tracing strategies OR mobile application � OR electronic device � OR population surveillance OR public health surveillance OR epidemiological monitoring OR infection control OR communicable disease control OR smartphone OR disease notification) was applied and adapted by two investigators on nine online databases (i.e.PubMed, Scopus, World Health Organization, Biomed Central, Web of Science, Cochrane Library, Chinese Center for Disease Control and Prevention, European Center for Disease Control and Prevention and Center for Disease Control and Prevention).Relevant articles were also identified from the reference list of excluded systematic reviews.The inclusion criteria were the following: (1) observational and modelling studies focusing on DCT of COVID-19 in the population, published in English from January 2020 to October 2021 and providing quantitative data.
The initial 18 months of the pandemic were considered as most countries discontinued the use of contact tracing devices thereafter, due to low adoption rates. 6Also, contact tracing among the general population ceased to be an effective strategy against the Omicron variant due to its high transmission rates; priority was given to high-risk settings (e.g.hospitals) and contacts (e.g.vulnerable populations) (2) mobile devices or web platforms used for DCT (3) population-based contact tracing, including nursing homes and long-term care facilities (4) modelling studies using real-world data or hypothetical populations Data extraction from the included studies was performed using an ad hoc extraction form, distinguishing population-based studies (real-world contact tracing) from modelling studies.The main sections of the form for population-based studies included the following: (1) general characteristics of the study (first author and year of publication, study setting, design and period, name and type of the contact tracing device/digital platform, technology employed, definition of contact and comparisons with other digital tools) (2) uptake rate by the population (percentage of persons who downloaded and actively used the app, and of all positive tests that occurred among app users) (3) security, ethical and privacy considerations (e.g.privacy from authorities and contacts, user consent; equity, harms from false positive/negative results; cyber attack protection through passwords, anonymization techniques, centralized or decentralized system).
Besides these sections, the extraction form for modelling studies included information about the type of model, study population, type of intervention (e.g.DCT alone or in combination with other measures, manual tracing, isolation, social distancing), and comparisons with other interventions (e.g.no intervention, lockdown, social distancing without contact tracing).Other aspects considered were sensitivity analysis, communication technologies used by the devices, data sources, privacy and security issues of the tools, definition of contacts, COVID-19 test specificity and sensitivity and public availability of the model code.
Besides the uptake rate in population-based studies, the effectiveness of DCT was evaluated for both study types using metrics adapted from the Indicator Framework 5 : (1) the number of close contacts of COVID-19 cases and of close contacts per index case identified (2) the number of laboratory-confirmed COVID-19 cases detected from close contacts (3) reduction of the effective reproduction number or reduction of COVID-19 infections.
The quality assessment of the studies was performed with the Effective Public Health Practice Project tool for population-based studies 11 and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist 12 for modelling studies.Questions not relevant to non-economic modelling studies were omitted from the CHEERS checklist (i.e. 1, 6, 8-14 and 19-21).
The results of the current study are presented as a qualitative synthesis, due to the heterogeneity of the included records.

Results
The search strategy identified 8743 records (figure 1), of which over 7000 were screened by title and abstract after duplicate removal.Fifty-eight full-text articles were assessed for eligibility with the elimination of 21 records, leaving 37 articles (13 population-based and 24 modelling studies) in the systematic review.

Population-based studies
Most population-based studies (10/13) were published in 2020 and in Asian countries (6/13) (table 1); one multinational study was conducted in Asia, Europe and the USA. 13The majority (9/13) had a cross-sectional design, of which two studies had also a cohort design.
The study population (data not presented in table 1) was the general population in all studies, except in one 14 with National Football League players and staff members as the main targets.The uptake rate of the devices was evaluated through the number of downloads (range 27.44-100%), active users (61.5-100%) and positive tests among app users (0-14.8%).All studies, except one, 13 provided a definition of contact that included physical distancing, duration and frequency of the encounters.
Smartphones, frequently in combination with other tracking systems, were the main tools evaluated in the studies (10/13), while wearable devices 14 and web-based monitoring tools 3 were examined in one study each.Data exchange between devices was based on Global Positioning System (GPS) and Bluetooth technologies.Geolocalization was performed through a combination of mobile phone operating systems (Android and Apple), closed-circuit television (CCTV), text messaging and electronic payment systems.Manual contact tracing was also evaluated (7/13).Comparisons were made, except in 9/13 studies, with other digital tools, traditional contact tracing or no contact tracing.Comparisons between interventions revealed the effectiveness of combining digital and traditional contact tracing.4]15 Also, the workload due to monitoring of infected close contacts decreases. 4,15,16The effectiveness of conventional epidemiological investigations in combination with DTC, isolation or testing was also observed. 14,17,18egarding the effectiveness of contact tracing, the number of identified close contacts ranged across the studies from 5 to 1.7 million, and from 0.24 to 16.5 close contacts per index case.The laboratory-confirmed COVID-19 cases detected from close contacts ranged from zero to 15.4%.The effective reproduction number was reported in one study: it was above six at the beginning of the outbreak and lower than one after the launch of the epidemiological i12 European Journal of Public Health investigation.Three studies indicated a substantial decrease in COVID-19 infections (table 1).
Information about privacy, ethical issues and security measures was available in nine, two, and five studies, respectively.The use of monitoring devices was mandatory in South Korea, Singapore and Taiwan.In Taiwan, after the outbreak of SARS in 2007, authorization or consent for the retrieval of personal information related to the outbreak of emerging infectious diseases, such as SARS-COV-2, could be waived.In South Korea, COVID-19-related data were collected as part of the epidemiological investigation of the Korean Centers for Disease Control and Prevention, and individual consent was not applicable.Centralized server systems for the storage and processing of the collected data were deployed in Italy, Singapore, the USA, China, Taiwan and the UK.Decentralized systems, less subjected to data breaches, were implemented in Switzerland, Japan and some States in the USA (table 1).

Modelling studies
The modelling studies (table 2) were conducted mostly in 2020 (16/24) and in Europe (8/24); the setting was not specified in seven studies.
Various models were deployed, including compartmental models such as Susceptible-Infected-Removed (SIR) and Susceptible-Exposed-Infectious-Removed (SEIR) and their adapted versions, agent-based models and individual-based models.The general population was the main target in 11/24 studies, followed by hypothetical (9/24) and synthetic populations (3/24).The interventions consisted of a combination of strategies in most cases, including digital or manual contact tracing and non-pharmaceutical interventions (e.g.social distancing, lockdowns, mask wearing and hand hygiene).Recursive [19][20][21][22] and bidirectional contact tracing 23 resulted as more effective than forwardtracing alone, albeit leading to the quarantine of a substantial number of healthy individuals.In recursive contact tracing, not only direct contacts are traced but also contacts of contacts while in bidirectional contact tracing, reverse tracing identifies the parent case who infected a known case and the process continues to discover other cases related to the parent case.The interventions under study were compared with multiple hypothetical scenarios, comprising no intervention (no contact tracing, testing or social distancing), manual/DCT or combined interventions.
According to the studies, the adoption rate of contact tracing devices is the main factor impacting the spread of an outbreak.As The role of digital tools and emerging devices in COVID- 19 i13 Taiwan     The role of digital tools and emerging devices in COVID-19   The role of digital tools and emerging devices in COVID-19 i23 larger proportions of the population adopt the devices, the spread of the virus is increasingly reduced; therefore, the benefits are extended to the wider population.5][26] However, DCT is still effective even with a 25% app adoption rate compared to no adoption.The use of geolocalization technologies, such as GPS, improves the estimation of the user's real-time location but might present privacy issues. 27To achieve better outbreak control, DCT should be combined with other measures, such as social distancing, mask wearing or COVID-19 testing and traditional contact tracing.The availability of fast testing and timely coordination of test results with contact tracing are important for the effectiveness of the interventions.As recursive and bidirectional contact tracing often leads to a quarantine of a large proportion of the healthy population, [19][20][21][22] among the benefits of combining DCT with other mitigation measures (e.g.21][22]28 Traditional contact tracing alone is not fast enough for the containment of the coronavirus.The delay between a case confirmation and contact identification is inevitable but could be mitigated by using a digital app.The lack of healthcare professionals is also a concern when the pandemic progresses. 29egarding other aspects of the modelling studies, sensitivity analysis on uncertain parameters was performed in 13/24 studies.The communication technologies were essentially Bluetooth (12/24), GPS (2/24) and a combination of proximity-sensing applications (1/24); the technology was not specified in 7/24 studies.A clear definition of contact was reported in 15/24 studies and mostly included physical distancing, duration and frequency of the encounters.COVID-19 test specificity was included in the model in one study, 28 while sensitivity was included in three studies. 23,28,30rivacy and security issues of the devices were also considered in the models (11/24).The data sources used for the model parameters were mainly literature studies, publicly available datasets from the national census and surveys; they were not reported in two studies. 31,32The codes used for the analysis are publicly available for 15/ 24 studies, mostly published on GitHub.

Quality assessment
The majority (9/13) of the population-based studies achieved a moderate quality level (with one weak section rating).Two studies obtained a strong global rating for not achieving weak ratings in any section.Weak global ratings were obtained by two studies due to bias in the study design and blinding sections.The sections 'confounders' and 'withdrawals and dropouts' were not applicable in all studies.
All modelling studies (table 3), except four, had a structured abstract summarizing all important elements and the introduction sections provided a broader context and relevance of the study.The target populations and subgroups were not always well analyzed or specified (8/24), as well as the setting (6/24).The analytic methods supporting the model were not described in one study. 27Values, ranges, references and probability distributions for all parameters were not reported in three of the articles and the sources of uncertainty were also lacking (4/24).The funding source was the least reported item (11/24), the role of the funding body was also missing in 6/24 articles.Three studies did not provide a conflict of interest statement.

Limitations
Limitations of the systematic review are inherent to the availability of some records only as preprints and to the lack of full-text articles that were eliminated from the study.Although this could impact the findings, the included records were informative and of medium to high quality, allowing a detailed assessment of the studies.Out of the two excluded preprints during the full-text assessment, only Plank et al. has been published in its definitive form in 2022.This raises concerns regarding the preprint's overall quality and its potential impact on the systematic reviews which encompassed it. 7,8Given that our review focused on the initial 18 months of the pandemic, the study published in 2022 was excluded.Nevertheless, its findings align with our research in terms of the effectiveness of DCT when combined with other strategies and the advantages of a high adoption rate of digital tools.
The uptake rates of the tools in the population-based studies (i.e. percentage of downloads, active app users and positive test among app users) were not referred to the entire population of the country under consideration but to the sample size of the study, which was not specified in all records.Only two studies reported data related to the entire population: the Swiss population 2 and the residents of England and Wales. 33Therefore, the percentages of downloads were reported or calculable in five studies, 2, [14][15][16]33 and in six studies each for active users 2-4,14,15,33 and positive test among active users.2,3,14,16,17, 33 The information was also reported differently in each study (e.g. the proportion of enrollees that accepted to download and use the app, percentage change of app users during the study and number or percentage of notified cases).This rendered synthesis and comparison of the uptake rate difficult. However, theeffectiveness of contact tracing and the level of implementation of the devices in specific settings were assessed with other available indicators, such as the number of identified close contacts of COVID-19 cases, close contacts per index case, laboratoryconfirmed COVID-19 cases detected from close contacts, the reproduction number and reduction of COVID-19 infections. A metaanalysis as not performed due to the heterogeneity of the studies.

Discussion
This study evaluated the role of digital tools deployed for contact tracing during the recent public health emergency, taking into consideration their effectiveness and impact on the population and healthcare providers.DCT is considered a valuable approach to limit the spread of SARS-CoV-2 but should be combined with other preventive measures and the population uptake must be high to contain the outbreaks.0][21][22] The combination of digital and traditional contact tracing renders the tracing process faster, reduces the workload for healthcare professionals and mitigates the societal and economic cost due to the quarantine of healthy subjects and required resources. 19,20,28,29DCT alone could effectively contain the pandemic only if the entire population uses digital devices and strictly adheres to preventive measures (e.g.5][26] However, even with a lower uptake rate, DCT still reduces the epidemic size compared to no adoption of the apps. 27It should be noted that two indicators used to evaluate the effectiveness of DCT (i.e. the number of close contacts of COVID-19 cases/ close contacts per index case; laboratory-confirmed COVID-19 cases detected from close contacts) may be affected by risk mitigation measures (e.g.quarantine, social distancing, testing, vaccination), which were adopted according to the changing epidemic situation and may have in part affected the results of the included studies.The range of the cases from close contacts reported by the studies is often very large.This could also be due to the pandemic period, thus to more or less aggressive circulating virus variants and the related risk mitigation measures adopted by the countries.
The main barriers to the wide implementation of emerging devices are the adoption rate 2, 24,27,28,33,34 and privacy and security issues. 2,13,15,16,27,31,33Several data security and privacy breaches have been registered worldwide, 35 and were related to data storage on a central server in centralized protocols presenting security and other technical limitations.Contrarily, decentralized systems allow data to be stored on individual devices avoiding such risk.Although  The role of digital tools and emerging devices in COVID-19

Figure 1
Figure 1 Flow diagram depicting the study selection procedure on digital contact tracing during the first 18 months of the COVID-19 pandemic

Table 1
General characteristics of the population-based studies included in the systematic review on COVID-19 digital contact tracing

Table 1 Continued
a: CI, confidence interval.b: nr, not reported.c: GPS, Global Positioning System.d: CCTV, closed-circuit television.e: KCDC, Korean Centers for Disease Control and Prevention.f: Supplementary material S1. g: GDPR, General Data Protection Regulation.h: NHS, National Health Service.i: iOS, iPhone Operating System.j: FOPH, the Swiss Federal Office of Public Health.k: EN, exposure notification.l: PHR, personal health record.m: na, not applicable.

Table 2
Main characteristics of the modelling studies included in the systematic review on COVID-19 digital contact tracing

Table 3
Quality assessment of modelling studies on COVID-19 digital contact tracing performed with the CHEERS a